Multimorbidity is when people suffer from more than one long-term illness. It is increasingly common as people live longer. It is important because individual illnesses have knock-on effects on others, it is more complex managing multiple than single illnesses, and multimorbid patients are heavy users of medications and health services.
To understand multimorbidity we need to know which illnesses tend to occur together and which illness combinations most affect health. To adapt health services we need to know which types of people develop multimorbidity: their age, sex, ethnicity and socio-economic status. Previous research grouped illnesses according to how commonly they occur together, without giving any special significance to combinations of illnesses linked to risk of death, hospital admission or other outcomes e.g. health service usage, quality of life. Clearly such combinations of illness are of more importance. There are more advanced analysis methods which can address these and other shortcomings.
We will develop methods of data analysis. We will review research on different statistical methods for grouping illnesses together. We will analyse largescale electronic health record data from CPRD Aurum to identify the groups of illnesses that co-occur. At the end of this step we will produce software to analyse and find groups of illnesses in electronic health records and make this freely available for other researchers to use.
We will work with patient advisors to help guide analysis of patients journeys through health services.
In this programme, to increase our understanding of multimorbidity (MM), we develop and implement state-of-the-art statistical methods for the analysis of electronic health records data. We will focus on identifying MM clusters and investigating their consequences. We will hold a stakeholder workshop to seek consensus on methods for investigating MM clusters including lists of conditions to use and clustering approach. As well as critically reviewing existing literature, we will construct new outcome-guided probabilistic clustering techniques to identify cross-sectional and longitudinal patterns of MM associated with clinically relevant outcomes. For the latter, we will produce new time-sequence kernel approaches for grouping sequences of acquired conditions and multi-state models that will additionally model time-to-condition (new comorbidity, hospitalisation or mortality) data. Landmarking approaches will be used to include trajectories of continuous biomarker data within these models to improve their prognostic utility. These methods will serve as the basis of novel clinical prediction tools that can be used to guide decision making. All our methods will be documented and made freely available via a methodological commons that will provide reproducible code notebooks and visualisation of findings in ways that aid clinical interpretation. We will conduct a detailed exploration of longitudinal implications of clusters for polypharmacy and prognosis. The utility of our methods will be validated using a separate national primary care database (The Health Improvement Network, THIN).
Mortality (ONS death date), acute hospitalisations, polypharmacy, GP consultations with a clinician or nurse, and the development of chronic conditions.
Sylvia Richardson - Chief Investigator - University of Cambridge
Jessica Barrett - Corresponding Applicant - University of Cambridge
Bemsibom Toh - Collaborator - University of Cambridge
Catherine Saunders - Collaborator - University of Cambridge
Christopher Yau - Collaborator - University of Birmingham
Duncan Edwards - Collaborator - University of Cambridge
Francesca Crowe - Collaborator - University of Birmingham
Krishnarajah Nirantharakumar - Collaborator - University of Birmingham
Magdalena Skrybant - Collaborator - University of Birmingham
Paul Kirk - Collaborator - University of Cambridge
Sida Chen - Collaborator - University of Cambridge
Simon Griffin - Collaborator - University of Cambridge
Steven Kiddle - Collaborator - AstraZeneca Ltd - UK Headquarters
Tom Marshall - Collaborator - University of Birmingham
Yajing Zhu - Collaborator - University of Cambridge
HES Admitted Patient Care;ONS Death Registration Data;Patient Level Index of Multiple Deprivation;Practice Level Index of Multiple Deprivation